The proposed research is to undertake empirical studies of the variations in the behaviors of NYC residents during the blackout in July, 1977. The objective of the research is to identify the characteristics of communities and their resident populations which explain and predict the variations in behaviors during the blackout. Empirical data for the studies will be obtained from individual interviews with residents and from information available in the public domain. The following are the specific aims of the research: a. To identify characteristics of communities and their resident populations which differentiate areas similar in socio-economic level but different in dangerous blackout behavior. b. To identify characteristics of random sampling of individuals which predict their reported behavior and feelings during the blackout. c. To identify the interaction effect of particular combinations of individuals and ecological variables which add to the prediction of behavior feelings and attitudes. d. To identify characteristics of specific subgroups of individuals (i.e., looters) which differentiate between comparison groups obtained by the random sampling of individuals. Communities within the five boroughs of N.Y.C. will be ranked to form five sets of health areas, which is the ecological unit of analysis. Ten health areas in which looting took place will be paired with the health areas most similar on the socieoeconimic status index but with an absence of looting behavior. The primary dependent-variable is the dichotomous variable describing health areas as looted versus not looted. Sets of independent variables thought to be theoretically linked to the dependent variable will be used in a series of multiple regression analyses to identify health areas associated with looting and burning. A second set of dependent variables will include behavior of residents and their reactions to the blackout situation. Here independent variables will include health area characteristics. Classes or sets of variables will be used to predict blackout reactions.